[AMD][DI][CI] 8/N Add GLM-5.2 MXFP4 1P1D DI/CI recipes (base + MTP + DP8/EP8) (#32120)
This commit is contained in:
Zhaoyi Li
@@ -532,3 +532,75 @@ kimik26-mxfp4-mi355x-ep16-mtp-sglang:
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search-space:
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- conc-list: [1, 8, 16, 32, 64, 128, 256]
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config_file: scripts/ci/slurm/recipes/mi355x-fp8/kimik26/1k1k/2p1d-ep16-mtp-mxfp4.yaml
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# AMD MI355X 2-node 1P1D disaggregation for GLM-5.2 (MXFP4) over MORI. GLM uses
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# GlmMoeDsaForCausalLM (DeepSeek Sparse Attention, auto-selected) with a built-in
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# NextN MTP head. Four variants mirroring the DeepSeek-V4 basic tier:
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# * (base) : TP8, no MTP
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# * -mtp : TP8 + NextN MTP
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# * -dp8ep8 : DP-attention 8 + narrow within-node EP8
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# * -dp8ep8-mtp : DP8 + narrow EP8 + NextN MTP
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# NOTE: checkpoint on disk is amd/GLM-5.1-MXFP4 (only GLM MXFP4 build mirrored).
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glm52-fp4-mi355x-sglang:
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model: amd/GLM-5.2-MXFP4
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model-prefix: glm52
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model_path: /it-share/model_coverage/models--amd--GLM-5.1-MXFP4
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runner: mi355x
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precision: fp4
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framework: sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [1, 8, 16, 32, 64, 128, 256]
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config_file: scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d.yaml
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glm52-fp4-mi355x-mtp-sglang:
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model: amd/GLM-5.2-MXFP4
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model-prefix: glm52
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model_path: /it-share/model_coverage/models--amd--GLM-5.1-MXFP4
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runner: mi355x
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precision: fp4
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framework: sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [1, 8, 16, 32, 64, 128, 256]
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config_file: scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d-mtp.yaml
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glm52-fp4-mi355x-dp8ep8-sglang:
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model: amd/GLM-5.2-MXFP4
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model-prefix: glm52
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model_path: /it-share/model_coverage/models--amd--GLM-5.1-MXFP4
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runner: mi355x
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precision: fp4
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framework: sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [1, 8, 16, 32, 64, 128, 256]
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config_file: scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d-dp8ep8.yaml
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glm52-fp4-mi355x-dp8ep8-mtp-sglang:
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model: amd/GLM-5.2-MXFP4
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model-prefix: glm52
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model_path: /it-share/model_coverage/models--amd--GLM-5.1-MXFP4
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runner: mi355x
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precision: fp4
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framework: sglang
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multinode: true
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disagg: true
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seq-len-configs:
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- isl: 1024
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osl: 1024
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search-space:
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- conc-list: [1, 8, 16, 32, 64, 128, 256]
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config_file: scripts/ci/slurm/recipes/mi355x-fp4/glm52/1k1k/1p1d-dp8ep8-mtp.yaml
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@@ -0,0 +1,80 @@
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# MI355X GLM-5.2 (MXFP4) 2-node 1P1D disaggregation recipe — DP8 + narrow EP8 + MTP.
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#
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# DSA attention is auto-selected (see 1p1d.yaml). DP-attention 8 + within-node
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# EP8 + built-in NextN MTP head (no external draft).
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#
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# Consumed by:
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# * scripts/ci/slurm/process_result.py reads `resources` and
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# `backend.sglang_config` (TP/EP/DP + worker counts) for the summary table.
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# * scripts/ci/slurm/launch_mi355x.sh reads `runtime`, `bench`, and `mtp`.
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resources:
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prefill_workers: 1
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decode_workers: 1
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backend:
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sglang_config:
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prefill:
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tensor-parallel-size: 8
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expert-parallel-size: 8
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data-parallel-size: 8
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decode:
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tensor-parallel-size: 8
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expert-parallel-size: 8
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data-parallel-size: 8
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# Model-specific docker env + sglang server args (generic launcher path; keeps
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# GLM off the hardcoded DeepSeek-V4 parser branch in launch_mi355x.sh). DSA
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# attention is auto-selected for GlmMoeDsaForCausalLM, so no --attention-backend
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# is set. GLM has a shared expert (n_shared_experts=1); shared-experts-fusion is
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# disabled to mirror the DeepSeek-V4 path.
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model:
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env:
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SGLANG_USE_AITER: 1
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server_args:
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- --reasoning-parser
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- glm45
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- --tool-call-parser
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- glm45
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- --disable-shared-experts-fusion
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runtime:
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image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260722
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# attention_backend intentionally unset: DSA is auto-selected for
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# GlmMoeDsaForCausalLM.
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# RoCE HCAs MORI uses for cross-node KV transfer.
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ib_devices: rdma0,rdma1,rdma2,rdma3
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prefill_port: 30025
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decode_port: 30026
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prefill_bootstrap_port: 8998
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decode_bootstrap_port: 9001
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lb_port: 8000
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mem_fraction_static: 0.90
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page_size: 256
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max_running_requests: 256
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chunked_prefill_size: 8192
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swa_full_tokens_ratio: 0.1
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# MTP / EAGLE speculative decoding (built-in NextN head from the base model).
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# Applied to both prefill and decode. No draft_model_path: the NextN head lives
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# in the base checkpoint.
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mtp:
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enabled: true
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num_steps: 3
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eagle_topk: 1
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num_draft_tokens: 4
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bench:
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# bench_serving --max-concurrency sweep; one result JSON per concurrency.
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concurrencies: [1, 8, 16, 32, 64, 128, 256]
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num_prompts_factor: 4 # num-prompts = concurrency * factor
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random_range_ratio: 1.0
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# Correctness gate run through the PD path before the perf sweep (full GSM8K,
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# 8-shot, accuracy > 0.91). A regression here fails the nightly even when
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# throughput looks fine ("fast but wrong").
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accuracy:
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enabled: true
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num_shots: 8
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num_questions: 1319 # full GSM8K test set
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threshold: 0.91
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@@ -0,0 +1,71 @@
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# MI355X GLM-5.2 (MXFP4) 2-node 1P1D disaggregation recipe — DP8 + narrow EP8.
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#
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# DSA attention is auto-selected (see 1p1d.yaml). DP-attention 8 + within-node
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# EP8, same topology as the DeepSeek-V4 dp8ep8 leg.
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#
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# Consumed by:
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# * scripts/ci/slurm/process_result.py reads `resources` and
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# `backend.sglang_config` (TP/EP/DP + worker counts) for the summary table.
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# * scripts/ci/slurm/launch_mi355x.sh reads `runtime`, `bench`, and `mtp`.
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resources:
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prefill_workers: 1
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decode_workers: 1
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backend:
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sglang_config:
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prefill:
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tensor-parallel-size: 8
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expert-parallel-size: 8
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data-parallel-size: 8
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decode:
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tensor-parallel-size: 8
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expert-parallel-size: 8
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data-parallel-size: 8
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# Model-specific docker env + sglang server args (generic launcher path; keeps
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# GLM off the hardcoded DeepSeek-V4 parser branch in launch_mi355x.sh). DSA
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# attention is auto-selected for GlmMoeDsaForCausalLM, so no --attention-backend
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# is set. GLM has a shared expert (n_shared_experts=1); shared-experts-fusion is
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# disabled to mirror the DeepSeek-V4 path.
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model:
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env:
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SGLANG_USE_AITER: 1
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server_args:
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- --reasoning-parser
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- glm45
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- --tool-call-parser
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- glm45
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- --disable-shared-experts-fusion
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runtime:
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image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260722
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# attention_backend intentionally unset: DSA is auto-selected for
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# GlmMoeDsaForCausalLM.
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# RoCE HCAs MORI uses for cross-node KV transfer.
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ib_devices: rdma0,rdma1,rdma2,rdma3
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prefill_port: 30025
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decode_port: 30026
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prefill_bootstrap_port: 8998
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decode_bootstrap_port: 9001
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lb_port: 8000
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mem_fraction_static: 0.90
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page_size: 256
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max_running_requests: 256
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chunked_prefill_size: 8192
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swa_full_tokens_ratio: 0.1
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bench:
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# bench_serving --max-concurrency sweep; one result JSON per concurrency.
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concurrencies: [1, 8, 16, 32, 64, 128, 256]
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num_prompts_factor: 4 # num-prompts = concurrency * factor
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random_range_ratio: 1.0
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# Correctness gate run through the PD path before the perf sweep (full GSM8K,
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# 8-shot, accuracy > 0.91). A regression here fails the nightly even when
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# throughput looks fine ("fast but wrong").
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accuracy:
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enabled: true
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num_shots: 8
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num_questions: 1319 # full GSM8K test set
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threshold: 0.91
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@@ -0,0 +1,81 @@
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# MI355X GLM-5.2 (MXFP4) 2-node 1P1D disaggregation recipe — TP8 + MTP.
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#
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# GLM-5.2 ships a built-in NextN MTP head (num_nextn_predict_layers=1), same
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# mechanism as DeepSeek-V4 — no external draft checkpoint. DSA attention is
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# auto-selected (see 1p1d.yaml).
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#
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# Consumed by:
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# * scripts/ci/slurm/process_result.py reads `resources` and
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# `backend.sglang_config` (TP/EP/DP + worker counts) for the summary table.
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# * scripts/ci/slurm/launch_mi355x.sh reads `runtime`, `bench`, and `mtp`.
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resources:
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prefill_workers: 1
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decode_workers: 1
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backend:
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sglang_config:
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prefill:
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tensor-parallel-size: 8
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expert-parallel-size: 1
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data-parallel-size: 1
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decode:
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tensor-parallel-size: 8
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expert-parallel-size: 1
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data-parallel-size: 1
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# Model-specific docker env + sglang server args (generic launcher path; keeps
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# GLM off the hardcoded DeepSeek-V4 parser branch in launch_mi355x.sh). DSA
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# attention is auto-selected for GlmMoeDsaForCausalLM, so no --attention-backend
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||||
# is set. GLM has a shared expert (n_shared_experts=1); shared-experts-fusion is
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# disabled to mirror the DeepSeek-V4 path.
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model:
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env:
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SGLANG_USE_AITER: 1
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server_args:
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- --reasoning-parser
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- glm45
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- --tool-call-parser
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- glm45
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- --disable-shared-experts-fusion
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||||
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runtime:
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image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260722
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# attention_backend intentionally unset: DSA is auto-selected for
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||||
# GlmMoeDsaForCausalLM.
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||||
# RoCE HCAs MORI uses for cross-node KV transfer.
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ib_devices: rdma0,rdma1,rdma2,rdma3
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prefill_port: 30025
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decode_port: 30026
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prefill_bootstrap_port: 8998
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decode_bootstrap_port: 9001
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lb_port: 8000
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mem_fraction_static: 0.90
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page_size: 256
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max_running_requests: 256
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chunked_prefill_size: 8192
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swa_full_tokens_ratio: 0.1
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# MTP / EAGLE speculative decoding (built-in NextN head from the base model).
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# Applied to both prefill and decode. No draft_model_path: the NextN head lives
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# in the base checkpoint.
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mtp:
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enabled: true
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num_steps: 3
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eagle_topk: 1
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num_draft_tokens: 4
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bench:
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# bench_serving --max-concurrency sweep; one result JSON per concurrency.
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concurrencies: [1, 8, 16, 32, 64, 128, 256]
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num_prompts_factor: 4 # num-prompts = concurrency * factor
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random_range_ratio: 1.0
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# Correctness gate run through the PD path before the perf sweep (full GSM8K,
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# 8-shot, accuracy > 0.91). A regression here fails the nightly even when
|
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# throughput looks fine ("fast but wrong").
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accuracy:
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enabled: true
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num_shots: 8
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num_questions: 1319 # full GSM8K test set
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threshold: 0.91
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@@ -0,0 +1,78 @@
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# MI355X GLM-5.2 (MXFP4) 2-node 1P1D disaggregation recipe.
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#
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# GLM-5.2 uses GlmMoeDsaForCausalLM (DeepSeek Sparse Attention). sglang
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# auto-selects the DSA attention backend for this architecture, so this recipe
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# leaves `attention_backend` unset (empty) and lets the server pick DSA. MXFP4
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# enables SGLANG_DSV4_FP4_EXPERTS in launch_mi355x.sh (driven by PRECISION),
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# same as the DeepSeek-V4 FP4 path.
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#
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# NOTE: the checkpoint on disk is amd/GLM-5.1-MXFP4 (only GLM MXFP4 build
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# currently mirrored on /it-share). model_path in nightly-configs.yaml points at
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# it; the recipe naming tracks the model line we are wiring CI for.
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#
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# Consumed by:
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# * scripts/ci/slurm/process_result.py reads `resources` and
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# `backend.sglang_config` (TP/EP/DP + worker counts) for the summary table.
|
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# * scripts/ci/slurm/launch_mi355x.sh reads `runtime` and `bench`.
|
||||
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resources:
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prefill_workers: 1
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decode_workers: 1
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backend:
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sglang_config:
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prefill:
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tensor-parallel-size: 8
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expert-parallel-size: 1
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data-parallel-size: 1
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decode:
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tensor-parallel-size: 8
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expert-parallel-size: 1
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data-parallel-size: 1
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# Model-specific docker env + sglang server args (generic launcher path; keeps
|
||||
# GLM off the hardcoded DeepSeek-V4 parser branch in launch_mi355x.sh). DSA
|
||||
# attention is auto-selected for GlmMoeDsaForCausalLM, so no --attention-backend
|
||||
# is set. GLM has a shared expert (n_shared_experts=1); shared-experts-fusion is
|
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# disabled to mirror the DeepSeek-V4 path.
|
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model:
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env:
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SGLANG_USE_AITER: 1
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server_args:
|
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- --reasoning-parser
|
||||
- glm45
|
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- --tool-call-parser
|
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- glm45
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- --disable-shared-experts-fusion
|
||||
|
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runtime:
|
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image: lmsysorg/sglang-rocm:v0.5.15.post1-rocm720-mi35x-20260722
|
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# attention_backend intentionally unset: DSA is auto-selected for
|
||||
# GlmMoeDsaForCausalLM.
|
||||
# RoCE HCAs MORI uses for cross-node KV transfer.
|
||||
ib_devices: rdma0,rdma1,rdma2,rdma3
|
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prefill_port: 30025
|
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decode_port: 30026
|
||||
prefill_bootstrap_port: 8998
|
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decode_bootstrap_port: 9001
|
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lb_port: 8000
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mem_fraction_static: 0.90
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page_size: 256
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max_running_requests: 256
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chunked_prefill_size: 8192
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swa_full_tokens_ratio: 0.1
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|
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bench:
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# bench_serving --max-concurrency sweep; one result JSON per concurrency.
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concurrencies: [1, 8, 16, 32, 64, 128, 256]
|
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num_prompts_factor: 4 # num-prompts = concurrency * factor
|
||||
random_range_ratio: 1.0
|
||||
|
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# Correctness gate run through the PD path before the perf sweep
|
||||
# (full GSM8K, 8-shot, accuracy > 0.91). A regression here fails the nightly
|
||||
# even when throughput looks fine ("fast but wrong").
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accuracy:
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enabled: true
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num_shots: 8
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num_questions: 1319 # full GSM8K test set
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threshold: 0.91
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||||
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